I have several datasets, each for a particular time point, and each containing several measures. For each of them, I want to conduct a one-sample t-test on each measure, so across all the columns. Each measure has a different mu value that I want to compare my results with. I have tried creating a function to do this so I only have to give it the name of the dataset as an argument. I have created a list of mu values. However, the function won't accept this and I get an error. Here is an example dataset:
t1 <- rnorm(20, 10, 1)
t2 <- rnorm(20, 10, 1)
t3 <- rnorm(20, 10, 1)
test_data <- data.frame(t1, t2, t3)
And the lists of mu values and variables:
muvals <- c(24, 51.8, 21.89)
varlist <- c(t1, t2, t3)
This is my attempt at the function:
onett <- function(tpoint) {
t.test(tpoint$varlist, mu = muvals)
}
And the error message I get is: Error in t.test.default(tpoint$varlist, mu = muvals) : 'mu' must be a single number
Is there a way to get this function to work, or otherwise iterate through each column and the list of mu values?
Edit: Each mu value only applies to one column. So the first value for the first column, etc.
To iterate over every combination of each column and mu value and simply print out the results of all t-tests the purrr::cross2 function would give you a list of all column/mu combinations and purrr::map would loop over the tests:
library(purrr)
t1 <- rnorm(20, 10, 1)
t2 <- rnorm(20, 10, 1)
t3 <- rnorm(20, 10, 1)
test_data <- data.frame(t1, t2, t3)
onett <- function(data) {
muvals <- c(24, 51.8, 21.89)
map(cross2(data, muvals), ~ t.test(.x[[1]], mu = .x[[2]]))
}
onett(test_data)
#> Prints t-test results...
From your clarification of question, it looks like map2 would do the simultaneous iteration over two objects the same length. To make a function you'd pass the data to, I'd suggest something like the following:
library(purrr)
library(dplyr)
library(tidyr)
t1 <- rnorm(20, 10, 1)
t2 <- rnorm(20, 10, 1)
t3 <- rnorm(20, 10, 1)
test_data <- data.frame(t1, t2, t3)
# (Can work best to have `muvals` defined in function rather than environment)
onett <- function(data, muvals = c(24, 51.8, 21.89)) {
map2(data, muvals, function(data, mu) t.test(data, mu = mu))
}
onett(test_data) %>%
map_dfr(broom::tidy)
#> # A tibble: 3 x 8
#> estimate statistic p.value parameter conf.low conf.high method alternative
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 10.1 -50.4 1.07e-21 19 9.50 10.7 One Samp~ two.sided
#> 2 10.3 -187. 1.65e-32 19 9.83 10.8 One Samp~ two.sided
#> 3 9.99 -47.8 2.87e-21 19 9.47 10.5 One Samp~ two.sided
The function outputs the list of t-test results. You can used broom::tidy to extract all t statistics, p-values etc. (shown above), or incorporate that into the function, or tidy the output within the function to give what you need.
Created on 2021-12-04 by the reprex package (v2.0.1)